Survey of Palm Print Detection Techniques
DOI:
https://doi.org/10.29304/jqcm.2022.14.4.1088Keywords:
Palm print, segmentation, machine learning, artificial neural networks, convolutional neural network, support vector machine, image processing, artificial intelligenceAbstract
Todays, there are various of systems that requires high-level security methods. Due to the sophisticated methods of breaking the traditional security methods. One of the most advanced methods nowadays is handprint validation. Which is based on the features of the palm in hands. These feature could include the lines, valleys, hand texture, and other features. In this work, a survey of the latest works that are used for palm print detection and recognition
Downloads
Download data is not yet available.
References
[1] W. Lidong and W. Guanghui, “Big Data in Cyber-Physical Systems, Digital Manufacturing and Industry 4.0,” Int. J. Eng. Manuf., vol. 6, no. 4, pp. 1–8, Jul. 2016, doi: 10.5815/ijem.2016.04.01.
[2] A. Bécue, I. Praça, and J. Gama, “Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities,” Artif. Intell. Rev., vol. 54, no. 5, pp. 3849–3886, Jun. 2021, doi: 10.1007/s10462-020-09942-2.
[3] A. Dhiman, K. Gupta, and D. K. Sharma, “An introduction to deep learning applications in biometric recognition,” in Trends in Deep Learning Methodologies, Elsevier, 2021, pp. 1–36.
[4] S. Trabelsi, D. Samai, F. Dornaika, A. Benlamoudi, K. Bensid, and A. Taleb-Ahmed, “Efficient palmprint biometric identification systems using deep learning and feature selection methods,” Neural Comput. Appl., vol. 34, no. 14, pp. 12119–12141, Jul. 2022, doi: 10.1007/s00521-022-07098-4.
[5] David Zhang, Wai-Kin Kong, Jane You, and Michael Wong, “Online palmprint identification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, pp. 1041–1050, Sep. 2003, doi: 10.1109/TPAMI.2003.1227981.
[6] Q. Xiao, J. Lu, W. Jia, and X. Liu, “Extracting Palmprint ROI From Whole Hand Image Using Straight Line Clusters,” IEEE Access, vol. 7, pp. 74327–74339, 2019, doi: 10.1109/ACCESS.2019.2918778.
[7] H. Shao, D. Zhong, and X. Du, “Efficient Deep Palmprint Recognition via Distilled Hashing Coding,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jun. 2019, pp. 714–723, doi: 10.1109/CVPRW.2019.00098.
[8] M. M. Ata, K. M. Elgamily, and M. A. Mohamed, “Toward Palmprint Recognition Methodology Based Machine Learning Techniques,” Eur. J. Electr. Eng. Comput. Sci., vol. 4, no. 4, Jul. 2020, doi: 10.24018/ejece.2020.4.4.225.
[9] X. Bao and Z. Guo, “Extracting region of interest for palmprint by convolutional neural networks,” in 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), Dec. 2016, pp. 1–6, doi: 10.1109/IPTA.2016.7820994.
[10] M. Izadpanahkakhk, S. Razavi, M. Taghipour-Gorjikolaie, S. Zahiri, and A. Uncini, “Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning,” Appl. Sci., vol. 8, no. 7, p. 1210, Jul. 2018, doi: 10.3390/app8071210.
[11] G. Jaswal, A. Kaul, R. Nath, and A. Nigam, “DeepPalm-A Unified Framework for Personal Human Authentication,” in 2018 International Conference on Signal Processing and Communications (SPCOM), Jul. 2018, pp. 322–326, doi: 10.1109/SPCOM.2018.8724419.
[12] Y. Liu and A. Kumar, “Contactless Palmprint Identification Using Deeply Learned Residual Features,” IEEE Trans. Biometrics, Behav. Identity Sci., vol. 2, no. 2, pp. 172–181, Apr. 2020, doi: 10.1109/TBIOM.2020.2967073.
[13] J. Chen, Y.-S. Moon, M.-F. Wong, and G. Su, “Palmprint authentication using a symbolic representation of images,” Image Vis. Comput., vol. 28, no. 3, pp. 343–351, Mar. 2010, doi: 10.1016/j.imavis.2009.06.004.
[14] R. Raghavendra and C. Busch, “Robust palmprint verification using sparse representation of binarized statistical features,” in Proceedings of the 2nd ACM workshop on Information hiding and multimedia security - IH&MMSec ’14, 2014, pp. 181–185, doi: 10.1145/2600918.2600929.
[15] Z. Guo, D. Zhang, L. Zhang, and W. Zuo, “Palmprint verification using binary orientation co-occurrence vector,” Pattern Recognit. Lett., vol. 30, no. 13, pp. 1219–1227, Oct. 2009, doi: 10.1016/j.patrec.2009.05.010.
[16] A. Ignat and I. Păvăloi, “Keypoint Selection Algorithm for Palmprint Recognition with SURF,” Procedia Comput. Sci., vol. 192, pp. 270–280, 2021, doi: 10.1016/j.procs.2021.08.028.
[17] A. Iula and M. Micucci, “A Feasible 3D Ultrasound Palmprint Recognition System for Secure Access Control Applications,” IEEE Access, vol. 9, pp. 39746–39756, 2021, doi: 10.1109/ACCESS.2021.3064638.
[18] L. Dian and S. Dongmei, “Contactless palmprint recognition based on convolutional neural network,” in 2016 IEEE 13th International Conference on Signal Processing (ICSP), Nov. 2016, pp. 1363–1367, doi: 10.1109/ICSP.2016.7878049.
[19] A. S. Tarawneh, D. Chetverikov, and A. B. Hassanat, “Pilot Comparative Study of Different Deep Features for Palmprint Identification in Low-Quality Images,” Apr. 2018, [Online]. Available: http://arxiv.org/abs/1804.04602.
[20] V. T., “Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature,” J. Innov. Image Process., vol. 3, no. 2, pp. 131–143, Jul. 2021, doi: 10.36548/jiip.2021.2.005.
[21] L. Wang et al., “Multispectral Palm Print and Palm Vein Acquisition Platform and Recognition Method Based on Convolutional Neural Network,” Comput. J., Mar. 2021, doi: 10.1093/comjnl/bxaa190.
[22] W. Jia, W. Xia, Y. Zhao, H. Min, and Y.-X. Chen, “2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search,” Int. J. Autom. Comput., vol. 18, no. 3, pp. 377–409, Jun. 2021, doi: 10.1007/s11633-021-1292-1.
[23] W. Gong, X. Zhang, B. Deng, and X. Xu, “Palmprint Recognition Based on Convolutional Neural Network-Alexnet,” Sep. 2019, pp. 313–316, doi: 10.15439/2019F248.
[24] K. Ito et al., “HandSegNet: Hand segmentation using convolutional neural network for contactless palmprint recognition,” IET Biometrics, Nov. 2021, doi: 10.1049/bme2.12058.
[2] A. Bécue, I. Praça, and J. Gama, “Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities,” Artif. Intell. Rev., vol. 54, no. 5, pp. 3849–3886, Jun. 2021, doi: 10.1007/s10462-020-09942-2.
[3] A. Dhiman, K. Gupta, and D. K. Sharma, “An introduction to deep learning applications in biometric recognition,” in Trends in Deep Learning Methodologies, Elsevier, 2021, pp. 1–36.
[4] S. Trabelsi, D. Samai, F. Dornaika, A. Benlamoudi, K. Bensid, and A. Taleb-Ahmed, “Efficient palmprint biometric identification systems using deep learning and feature selection methods,” Neural Comput. Appl., vol. 34, no. 14, pp. 12119–12141, Jul. 2022, doi: 10.1007/s00521-022-07098-4.
[5] David Zhang, Wai-Kin Kong, Jane You, and Michael Wong, “Online palmprint identification,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 9, pp. 1041–1050, Sep. 2003, doi: 10.1109/TPAMI.2003.1227981.
[6] Q. Xiao, J. Lu, W. Jia, and X. Liu, “Extracting Palmprint ROI From Whole Hand Image Using Straight Line Clusters,” IEEE Access, vol. 7, pp. 74327–74339, 2019, doi: 10.1109/ACCESS.2019.2918778.
[7] H. Shao, D. Zhong, and X. Du, “Efficient Deep Palmprint Recognition via Distilled Hashing Coding,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jun. 2019, pp. 714–723, doi: 10.1109/CVPRW.2019.00098.
[8] M. M. Ata, K. M. Elgamily, and M. A. Mohamed, “Toward Palmprint Recognition Methodology Based Machine Learning Techniques,” Eur. J. Electr. Eng. Comput. Sci., vol. 4, no. 4, Jul. 2020, doi: 10.24018/ejece.2020.4.4.225.
[9] X. Bao and Z. Guo, “Extracting region of interest for palmprint by convolutional neural networks,” in 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), Dec. 2016, pp. 1–6, doi: 10.1109/IPTA.2016.7820994.
[10] M. Izadpanahkakhk, S. Razavi, M. Taghipour-Gorjikolaie, S. Zahiri, and A. Uncini, “Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning,” Appl. Sci., vol. 8, no. 7, p. 1210, Jul. 2018, doi: 10.3390/app8071210.
[11] G. Jaswal, A. Kaul, R. Nath, and A. Nigam, “DeepPalm-A Unified Framework for Personal Human Authentication,” in 2018 International Conference on Signal Processing and Communications (SPCOM), Jul. 2018, pp. 322–326, doi: 10.1109/SPCOM.2018.8724419.
[12] Y. Liu and A. Kumar, “Contactless Palmprint Identification Using Deeply Learned Residual Features,” IEEE Trans. Biometrics, Behav. Identity Sci., vol. 2, no. 2, pp. 172–181, Apr. 2020, doi: 10.1109/TBIOM.2020.2967073.
[13] J. Chen, Y.-S. Moon, M.-F. Wong, and G. Su, “Palmprint authentication using a symbolic representation of images,” Image Vis. Comput., vol. 28, no. 3, pp. 343–351, Mar. 2010, doi: 10.1016/j.imavis.2009.06.004.
[14] R. Raghavendra and C. Busch, “Robust palmprint verification using sparse representation of binarized statistical features,” in Proceedings of the 2nd ACM workshop on Information hiding and multimedia security - IH&MMSec ’14, 2014, pp. 181–185, doi: 10.1145/2600918.2600929.
[15] Z. Guo, D. Zhang, L. Zhang, and W. Zuo, “Palmprint verification using binary orientation co-occurrence vector,” Pattern Recognit. Lett., vol. 30, no. 13, pp. 1219–1227, Oct. 2009, doi: 10.1016/j.patrec.2009.05.010.
[16] A. Ignat and I. Păvăloi, “Keypoint Selection Algorithm for Palmprint Recognition with SURF,” Procedia Comput. Sci., vol. 192, pp. 270–280, 2021, doi: 10.1016/j.procs.2021.08.028.
[17] A. Iula and M. Micucci, “A Feasible 3D Ultrasound Palmprint Recognition System for Secure Access Control Applications,” IEEE Access, vol. 9, pp. 39746–39756, 2021, doi: 10.1109/ACCESS.2021.3064638.
[18] L. Dian and S. Dongmei, “Contactless palmprint recognition based on convolutional neural network,” in 2016 IEEE 13th International Conference on Signal Processing (ICSP), Nov. 2016, pp. 1363–1367, doi: 10.1109/ICSP.2016.7878049.
[19] A. S. Tarawneh, D. Chetverikov, and A. B. Hassanat, “Pilot Comparative Study of Different Deep Features for Palmprint Identification in Low-Quality Images,” Apr. 2018, [Online]. Available: http://arxiv.org/abs/1804.04602.
[20] V. T., “Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature,” J. Innov. Image Process., vol. 3, no. 2, pp. 131–143, Jul. 2021, doi: 10.36548/jiip.2021.2.005.
[21] L. Wang et al., “Multispectral Palm Print and Palm Vein Acquisition Platform and Recognition Method Based on Convolutional Neural Network,” Comput. J., Mar. 2021, doi: 10.1093/comjnl/bxaa190.
[22] W. Jia, W. Xia, Y. Zhao, H. Min, and Y.-X. Chen, “2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search,” Int. J. Autom. Comput., vol. 18, no. 3, pp. 377–409, Jun. 2021, doi: 10.1007/s11633-021-1292-1.
[23] W. Gong, X. Zhang, B. Deng, and X. Xu, “Palmprint Recognition Based on Convolutional Neural Network-Alexnet,” Sep. 2019, pp. 313–316, doi: 10.15439/2019F248.
[24] K. Ito et al., “HandSegNet: Hand segmentation using convolutional neural network for contactless palmprint recognition,” IET Biometrics, Nov. 2021, doi: 10.1049/bme2.12058.
Downloads
Published
2022-12-02
How to Cite
Naji, N. H., & Al-Juboor, A. M. (2022). Survey of Palm Print Detection Techniques. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(4), Comp Page 74–81. https://doi.org/10.29304/jqcm.2022.14.4.1088
Issue
Section
Computer Articles
